AIMC Topic: Image Interpretation, Computer-Assisted

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A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology.

Journal of X-ray science and technology Jan 1, 2017
PURPOSE: To develop and test a deep learning based computer-aided diagnosis (CAD) scheme of mammograms for classifying between malignant and benign masses.
Neural Networks, Computer Machine Learning Algorithms Female Humans Breast Neoplasms Mammography Image Interpretation, Computer-Assisted
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Stationary Wavelet Transform and AdaBoost with SVM Based Pathological Brain Detection in MRI Scanning.

CNS & neurological disorders drug targets Jan 1, 2017
This paper presents an automatic classification system for segregating pathological brain from normal brains in magnetic resonance imaging scanning. The proposed system employs contrast limited adaptive histogram equalization scheme to enhance the di...
Wavelet Analysis Sensitivity and Specificity Support Vector Machine Humans Models, Neurological Pattern Recognition, Automated Brain Magnetic Resonance Imaging Brain Diseases Computer Simulation Image Interpretation, Computer-Assisted
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Differentiation of Glioblastoma and Lymphoma Using Feature Extraction and Support Vector Machine.

CNS & neurological disorders drug targets Jan 1, 2017
Differentiation of glioblastoma multiformes (GBMs) and lymphomas using multi-sequence magnetic resonance imaging (MRI) is an important task that is valuable for treatment planning. However, this task is a challenge because GBMs and lymphomas may have...
Retrospective Studies Sensitivity and Specificity Brain Neoplasms Imaging, Three-Dimensional Humans Brain Diagnosis, Differential Glioblastoma Nonlinear Dynamics Image Interpretation, Computer-Assisted Support Vector Machine Lymphoma Magnetic Resonance Imaging
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Adapting to Artificial Intelligence: Radiologists and Pathologists as Information Specialists.

JAMA Dec 13, 2016
Radiographic Image Interpretation, Computer-Assisted Pathologists Data Mining Information Services Artificial Intelligence Computers, Mainframe Machine Learning Humans Radiology Image Interpretation, Computer-Assisted Radiologists Pattern Recognition, Automated
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Machine Learning Applied to Alzheimer Disease.

Radiology Dec 1, 2016
Alzheimer Disease Image Interpretation, Computer-Assisted Humans Machine Learning Magnetic Resonance Imaging
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Label-aligned multi-task feature learning for multimodal classification of Alzheimer's disease and mild cognitive impairment.

Brain imaging and behavior Dec 1, 2016
Multimodal classification methods using different modalities of imaging and non-imaging data have recently shown great advantages over traditional single-modality-based ones for diagnosis and prognosis of Alzheimer's disease (AD), as well as its prod...
Alzheimer Disease Positron-Emission Tomography Image Interpretation, Computer-Assisted Pattern Recognition, Automated ROC Curve Multimodal Imaging Radiopharmaceuticals Fluorodeoxyglucose F18 Neuroimaging Machine Learning Databases, Factual Brain Disease Progression Cognitive Dysfunction Prognosis Aged Humans
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Neural networks improve brain cancer detection with Raman spectroscopy in the presence of operating room light artifacts.

Journal of biomedical optics Sep 1, 2016
Invasive brain cancer cells cannot be visualized during surgery and so they are often not removed. These residual cancer cells give rise to recurrences.
Humans Neural Networks, Computer Spectrum Analysis, Raman Machine Learning Image Interpretation, Computer-Assisted Artifacts Brain Operating Rooms Brain Neoplasms
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Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer's disease.

Brain imaging and behavior Sep 1, 2016
The study of brain networks by resting-state functional magnetic resonance imaging (rs-fMRI) is a promising method for identifying patients with dementia from healthy controls (HC). Using graph theory, different aspects of the brain network can be ef...
Male Cognitive Dysfunction Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Databases as Topic Brain Mapping Alzheimer Disease Female Aged Sensitivity and Specificity Humans Machine Learning Brain Rest Neural Pathways
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Classification of large-scale fundus image data sets: a cloud-computing framework.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Aug 1, 2016
Large medical image data sets with high dimensionality require substantial amount of computation time for data creation and data processing. This paper presents a novel generalized method that finds optimal image-based feature sets that reduce comput...
Fundus Oculi Image Interpretation, Computer-Assisted Female Humans Databases as Topic Diabetic Retinopathy Cloud Computing Reproducibility of Results Algorithms ROC Curve
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Automatic detection of neovascularization on optic disk region with feature extraction and support vector machine.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Aug 1, 2016
Neovascularization (NV) is a definitive indicator for the onset of Proliferative Diabetic Retinopathy (PDR). The new vessels are fragile and prone to bleed, leading to high risk of sudden vision loss. Automatic detection of NV is an important task in...
Image Interpretation, Computer-Assisted Diabetic Retinopathy Optic Disk Algorithms Humans Image Processing, Computer-Assisted Support Vector Machine Neovascularization, Pathologic
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